A novel approach to diagnosis of defective equipments In GIS using self organizing map

Author

Aggarwal, Raj ; Lin, Tao ; Kim, Chul Hwan

Author_Institution

Dept. of Electron. & Electr. Eng., Bath Univ., UK

fYear

2004

fDate

6-10 June 2004

Firstpage

362

Abstract

The condition monitoring (CM) for the gas insulated switchgear (GIS) requires an accurate and reliable identification of the defective equipments in it for maintenance purposes. In this paper, a feature extraction procedure is explored, which is based on the power spectra density (PSD) of the de-noised partial discharges (PDs) emanating from the defective equipments in GIS. Furthermore, artificial intelligence techniques, in particular, the self organizing map (SOM) are investigated for the role as classifier to precisely identify these defective equipments, based on the PSD feature patterns. The performance of the SOM based classifier is ascertained by using the PDs acquired from practical GISs on South Korean 154 kV EHV transmission networks.

Keywords

artificial intelligence; condition monitoring; feature extraction; gas insulated switchgear; partial discharges; power engineering computing; self-organising feature maps; transmission networks; 154 kV; EHV transmission network; GIS; artificial intelligence technique; condition monitoring; defective equipments diagnosis; feature extraction; gas insulated switchgear; partial discharge; power spectra density; self organizing map; Artificial intelligence; Artificial neural networks; Attenuation; Condition monitoring; Geographic Information Systems; Maintenance; Organizing; Signal processing; Surface fitting; Switchgear;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society General Meeting, 2004. IEEE

Print_ISBN

0-7803-8465-2

Type

conf

DOI

10.1109/PES.2004.1372813

Filename

1372813